Abstract Machine learning systems trained on user-provided data are susceptible to data
poisoning attacks, whereby malicious users inject false training data with the aim of …

A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

In vivo calcium imaging through microendoscopic lenses enables imaging of previously
inaccessible neuronal populations deep within the brains of freely moving animals …

This paper studies the problem of distributed stochastic optimization in an adversarial setting
where, out of $ m $ machines which allegedly compute stochastic gradients every iteration …

In high dimensions, most machine learning methods are brittle to even a small fraction of
structured outliers. To address this, we introduce a new meta-algorithm that can take in a …

In this paper, we study a simple and generic framework to tackle the problem of learning
model parameters when a fraction of the training samples are corrupted. Our approach is …